{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T15:49:29Z","timestamp":1761061769731,"version":"3.37.3"},"reference-count":30,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,6]]},"DOI":"10.1109\/cdc51059.2022.9992793","type":"proceedings-article","created":{"date-parts":[[2023,1,10]],"date-time":"2023-01-10T19:26:56Z","timestamp":1673378816000},"page":"4571-4578","source":"Crossref","is-referenced-by-count":4,"title":["Stochastic Gradient Tracking Methods for Distributed Personalized Optimization over Networks"],"prefix":"10.1109","author":[{"given":"Yan","family":"Huang","sequence":"first","affiliation":[{"name":"Zhejiang University,College of Control Science and Engineering,Hangzhou,China"}]},{"given":"Jinming","family":"Xu","sequence":"additional","affiliation":[{"name":"Zhejiang University,College of Control Science and Engineering,Hangzhou,China"}]},{"given":"Wenchao","family":"Meng","sequence":"additional","affiliation":[{"name":"Zhejiang University,College of Control Science and Engineering,Hangzhou,China"}]},{"given":"Hoi-To","family":"Wai","sequence":"additional","affiliation":[{"name":"The Chinese University of Hong Kong,Department of Systems Engineering and Engineering Management,Hong Kong,China"}]}],"member":"263","reference":[{"key":"ref1","first-page":"1273","article-title":"Communication-efficient learning of deep networks from decentralized data","volume-title":"Artificial intelligence and statistics","author":"McMahan","year":"2017"},{"article-title":"Federated optimization: Distributed machine learning for on-device intelligence","year":"2016","author":"Kone\u010dn\u1ef3","key":"ref2"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295285"},{"article-title":"Federated learning for mobile keyboard prediction","year":"2018","author":"Hard","key":"ref4"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/WorldS450073.2020.9210355"},{"article-title":"Stochastic primal-dual methods and sample complexity of reinforcement learning","year":"2016","author":"Chen","key":"ref6"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IROS47612.2022.9981256"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2008.2009515"},{"key":"ref9","article-title":"Federated multi-task learning","volume":"30","author":"Smith","year":"2017","journal-title":"Advances in neural information processing systems"},{"article-title":"Federated learning of a mixture of global and local models","year":"2020","author":"Hanzely","key":"ref10"},{"key":"ref11","first-page":"3","article-title":"On the convergence of federated optimization in heterogeneous networks","volume":"3","author":"Sahu","year":"2018"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i9.16960"},{"article-title":"Personalized federated learning: A unified framework and universal optimization techniques","year":"2021","author":"Hanzely","key":"ref13"},{"key":"ref14","first-page":"2304","article-title":"Lower bounds and optimal algorithms for personalized federated learning","volume":"33","author":"Hanzely","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Federated learning with personalization layers","year":"2019","author":"Arivazhagan","key":"ref15"},{"key":"ref16","article-title":"Federated reconstruction: Partially local federated learning","volume":"34","author":"Singhal","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"article-title":"Think locally, act globally: Federated learning with local and global representations","year":"2020","author":"Liang","key":"ref17"},{"key":"ref18","first-page":"2089","article-title":"Exploiting shared representations for personalized federated learning","volume-title":"International Conference on Machine Learning","author":"Collins"},{"article-title":"Adaptive federated optimization","volume-title":"International Conference on Learning Representations","author":"Reddi","key":"ref19"},{"key":"ref20","first-page":"3557","article-title":"Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach","volume":"33","author":"Fallah","year":"2020","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837896"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2015.7402509"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/TSIPN.2016.2524588"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1007\/s10107-020-01487-0"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/CDC.2009.5399485"},{"key":"ref26","first-page":"5132","article-title":"Scaffold: Stochastic controlled averaging for federated learning","volume-title":"International Conference on Machine Learning","author":"Karimireddy"},{"key":"ref27","article-title":"Multi-agent reinforcement learning via double averaging primal-dual optimization","volume":"31","author":"Wai","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2020.3008605"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1137\/16m1080173"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1561\/2200000050"}],"event":{"name":"2022 IEEE 61st Conference on Decision and Control (CDC)","start":{"date-parts":[[2022,12,6]]},"location":"Cancun, Mexico","end":{"date-parts":[[2022,12,9]]}},"container-title":["2022 IEEE 61st Conference on Decision and Control (CDC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9992315\/9992317\/09992793.pdf?arnumber=9992793","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T12:15:49Z","timestamp":1706789749000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9992793\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,6]]},"references-count":30,"URL":"https:\/\/doi.org\/10.1109\/cdc51059.2022.9992793","relation":{},"subject":[],"published":{"date-parts":[[2022,12,6]]}}}